About Dataset¶

Bloomberg Billionaires Index View profiles for each of the world’s 500 richest people, see the biggest movers, and compare fortunes or track returns.? As of December 12, 2024 The Bloomberg Billionaires Index is a daily ranking of the world’s richest people. Details about the calculations are provided in the net worth analysis on each billionaire’s profile page. The figures are updated at the close of every trading day in New York. Rank Name Total net worth "Last change" "YTD change" "Country / Region"

Sourse: https://www.kaggle.com/datasets/mahmoudredagamail/the-worlds-500-most-powerful-businessmen

Author of dataset: https://www.kaggle.com/mahmoudredagamail/datasets

Licences CC: https://creativecommons.org/licenses/by/4.0/

Changes Made for the Purpose of Analysis¶

  • In the original dataset, numbers were rounded and represented using thousands with the letter "B". These values have been adjusted for easier calculations.
  • Additionally, the "Country / Region" column was split into "Country" and "Region" to provide a more general and convenient view of the data while preserving detail.
  • Initially, Region was intended to represent continents, but this was not considered the best approach. Regions such as China, India, and Russia, due to their unique economic and cultural characteristics, deserve their own separate category. Even the populations of China and India are already counted in billions.
  • The original dataset only contained country names, so I created a Country_id column using the country_converter library and based on this, generated the Region column.
  • For a brief analysis of female billionaires, the regions China and Asia were excluded due to the difficulty of accurately identifying gender from first names.
  • A separate First Name column was created for analysis, which was used to determine gender using the gender_guesser library. The original Name column was renamed to Full Name.
  • For some unidentified or incorrectly classified genders in regions such as Africa and India, a short manual review was performed.
  • Phrases like "& family" in names of individuals from South America were removed to simplify the analysis.
  • The original dataset is also available.

Goals of the Analysis¶

The analysis of the top 500 billionaires focuses on the following key aspects:

  1. Geography

    • Number of billionaires per country and per region.

  2. Industry Breakdown

    • Number of billionaires in each industry.
    • Total net worth by industry.
    • Year-to-date (YTD) net income by industry.

  3. Gender Analysis (excluding Asia and China)

    • Ratio of Ladies vs Gentlemen.
    • Number of female billionaires per region.
    • Number of male billionaires per region.
    • Number of female billionaires per industry.

  4. Statistical Insights

    • Correlation between different financial metrics.
    • Identification of outliers in net worth and income.

STEP 1.1: General Overview of the Data¶

Basic info about data¶

Missing Data¶

column missing_values dtype
Rank 0 int64
Full Name 0 object
First Name 0 object
Total net worth 0 float64
$ Last change 0 float64
$ YTD change 0 float64
Country 0 object
Industry 0 object
Region 0 object
Country_id 0 object

Columns: 10
Rows: 500

Unique values¶

column unique_count
Rank 500
Full Name 500
First Name 354
Total net worth 318
$ Last change 373
$ YTD change 412
Country 48
Industry 14
Region 9
Country_id 48

Unique values in total¶

Unique values in total: 2576¶

Missing values¶

index 0
0 Rank 0
1 Full Name 0
2 First Name 0
3 Total net worth 0
4 $ Last change 0
5 $ YTD change 0
6 Country 0
7 Industry 0
8 Region 0
9 Country_id 0

Missing Valiues in total: 0¶

Duplicats¶

Duplicats: 0¶

Descriptive Statistics¶

count mean std min 25% 50% 75% max
Rank 500.00 250.50 144.48 1.00 125.75 250.50 375.25 500.00
Total net worth 500.00 20,108,140,000.00 33,819,339,606.25 6,380,000,000.00 7,927,500,000.00 11,000,000,000.00 17,525,000,000.00 447,000,000,000.00
$ Last change 500.00 194,894,426.00 2,902,651,485.03 -7,410,000,000.00 -59,300,000.00 0.00 64,725,000.00 62,800,000,000.00
$ YTD change 500.00 3,550,333,800.00 13,506,312,283.81 -26,400,000,000.00 323,500,000.00 1,395,000,000.00 3,150,000,000.00 218,000,000,000.00

Summary of the General Overview of the Data¶

Ther is:¶

Columns: 10

Rows: 500

industrys: 0 Rank 1 Full Name 2 First Name 3 Total net worth 4 $ Last change 5 $ YTD change 6 Country 7 Industry 8 Region 9 Country_id Name: column, dtype: object

  • 500 names of Bilioners
  • 14 industrys
  • 48 countrys
  • 9 main regions

Unique values in total: 2576

Missing Valiues in total: 0

Duplicats: 0

Required Column Transformation:¶

The Total net worth values are provided as strings in a shortened format — they need to be modified to allow for computations and converted to a numeric type.

We also observe negative values in YTD and Last Change — these should be converted to numeric type as well.

std is very hight in all money columns

STEP 2: Single Variable Analysis¶

Barplot for number of biloners for Country¶

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As we can see, the division into regions is necessary due to the fragmentation of billionaires across countries. These individuals are not very common.¶

At the same time, we notice that the distribution follows a "long tail pattern," with a dominant share belonging to the United States. This comes as no surprise, as the USA is the cradle of modern capitalism.¶

Barplot for number of biloners for Region¶

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As we can see, regional grouping based on cultural and geopolitical patterns greatly simplifies the picture of the global distribution of billionaires while maintaining clarity.¶

Barplot for Industry¶

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The chart is clearly left-skewed, showing a noticeable dominance of billionaires in the Technology, Finance, and Industrial sectors.¶

Total Net Worth of Millionaires by Industry¶

  Industry Total net worth
0 Technology 3142060000000.000000
1 Industrial 970580000000.000000
2 Finance 945160000000.000000
3 Retail 917680000000.000000
4 Diversified 908430000000.000000
5 Consumer 819120000000.000000
6 Energy 495920000000.000000
7 Food & Beverage 378540000000.000000
8 Health Care 344130000000.000000
9 Real Estate 306780000000.000000
10 Commodities 292350000000.000000
11 Media & Telecom 214820000000.000000
12 Services 167940000000.000000
13 Entertainment 150560000000.000000
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There is a massive dominance of the wealth held by billionaires in the technology sector — 31.3% compared to other billionaires, where the distribution, although left-skewed, is more moderate.¶

YTD net income of Millionaires in a given industry¶

  Industry $ YTD change
0 Technology 962940000000.000000
1 Retail 219942100000.000000
2 Finance 213054700000.000000
3 Industrial 104392000000.000000
4 Diversified 65666000000.000000
5 Energy 46957000000.000000
6 Health Care 44356600000.000000
7 Services 36488000000.000000
8 Real Estate 31755000000.000000
9 Media & Telecom 29018000000.000000
10 Entertainment 21596000000.000000
11 Food & Beverage 5649000000.000000
12 Consumer -3236000000.000000
13 Commodities -3411499999.999999
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Ratio of Ladys Billionaires to Gentemen Billionaires¶

For the purposes of the analysis, two "cultural-economic" regions, Asia and China, were excluded due to the difficulty of identifying gender based on first names.¶

Ladys and Gentelmen Ratio (No Asia nd China)¶

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Ladys make up (15.9%) 62 of all billionaires 389 (399). (No Asia and China)¶

Schedule of Ladys Bilonare in each Region (No Asia and China)¶

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The total number of millionaires in a given Region (No Asia nd China)

  Region Ladys
0 North America 38
1 Europe 19
2 Oceania 2
3 India 1
4 South America 1
5 Russian Federation 1
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  Industry $ YTD change
0 Retail 65000000000.000000
1 Energy 18510000000.000000
2 Technology 12680000000.000000
3 Finance 10769700000.000000
4 Industrial 8232000000.000000
5 Media & Telecom 6566000000.000000
6 Services 5510000000.000000
7 Entertainment 5250000000.000000
8 Diversified 5140000000.000000
9 Health Care 3117300000.000000
10 Commodities 2752000000.000001
11 Food & Beverage -4787000000.000000
12 Consumer -18019000000.000000
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Back to All Regions and ALL Bilioners¶

STEP 4: Correlations¶

Total net worth $ Last change $ YTD change
Total net worth 1.00 0.65 0.82
$ Last change 0.65 1.00 0.79
$ YTD change 0.82 0.79 1.00

Correlation Matrix¶

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Interactive scaterplot¶

STEP 5: Outlier Analysis¶

Boxplot of Total Billionaire Net Worth of the estate¶

Boxplot of Total Net Worth of Billionaires by Industry¶

Boxplot of annual net worth income of billionaires ($ YTD change) by industry¶

Boxplot of last change net worth ($ Last change) for industries¶

Column: Total net worth have 55 outliers.
Column: $ Last change have 83 outliers.
Column: $ YTD change have 63 outliers.

Summary of the overall analysis¶

Conclusions:¶

First Glance Regarding industries:

The largest number of billionaires operates in the technology industry. Specifically, 83 individuals, representing 16.6% of the dataset population. Interestingly, they collectively hold 31.3% of the wealth, amounting to 3,142.1 billion USD out of the total 500 billionaires. Their annual income in 2024 was 962.94 billion USD, constituting an impressive 54.2% of the total income of all 500 billionaires. Regarding total wealth across industries: We can distinguish four groups based on their total wealth, categorized as follows:

A. From 9.7% to 8.1%:

  • Industrial
  • Financial
  • Retail
  • Diversified

B. 4.9%:

  • Energy

C. From 3.8% to 2.9%:

  • Food & Beverages
  • Healthcare
  • Real Estate
  • Commodities

D. From 2.1% to 1.5%:

  • Media & Telecommunications
  • Services
  • Entertainment

Regarding billionares net income:¶

In addition to the technology sector, two other industries stand out:

  • Retail: 219.94 billion USD, 12%
  • Financial: 213.05 billion USD, 12%

Following them:

  • Industrial: 104.39 billion USD, 5.9%

Other industries:

  • Diversified
  • Energy
  • Healthcare
  • Services
  • Real Estate
  • Media & Telecommunications
  • Entertainment range from 3.7% to 1.2%.

The last industry that generated a profit:

  • Food & Beverages: $5.65 billion USD, 0.3%

Industries with negative income:

  • Consumer: -3.24 billion USD, -0.2%
  • Commodities: -3.41 billion USD, -0.2%

Correlations:¶

Very strong correlation between: "YTD change" and "Total net worth" at 0.82.

Strong correlation between: "YTD change" and "Last change" at 0.79

Moderately strong correlation between: "Last change" and "Total net worth" at 0.65.

Trends:¶

Based on the charts, the following trends can be observed:

There is a noticeable negative correlation between "Total net worth" and "$ YTD change" in the "Consumer" industry, as well as a less pronounced negative correlation in "Food & Beverages".

Positive but weak correlation exists in industries:

  • Diversified
  • Energy
  • Industrial
  • Real Estate
  • Commodities
  • Entertainment

In other industries, the correlation is clearly positive.

Outliers:¶

  • "Total net worth" has 55 outliers.
  • "Last change" has 83 outliers.
  • "YTD change" has 63 outliers.

No outliers were observed in the following cases:

Total net worth for billionaires in two industries:

  • Commodities
  • Real Estate

Annual net income of billionaires in four industries:

  • Food & Beverages
  • Entertainment
  • Real Estate
  • Services

Changes in last net worth:

No outliers were detected for two industries:

  • Commodities
  • Real Estate

Analysis of Ladys Billionaires (Excluding Asia and China)¶

Ladys billionaires represent 15.9% of all billionaires, totaling 62 out of 399 individuals in the dataset (excluding Asia and China).

The Industrial sector has the highest number of Ladys billionaires, with 8 Ladys (12.9%), whereas Entertainment and Services have the fewest, with only 2 women each (3.2%).

When looking at annual net income (YTD), Ladysbillionaires achieved the highest earnings in the Retail sector, totaling 65.05 USD billion (53.8%), while the lowest was in Commodities, with 2.78 USD billion (2.3%).

Some sectors even recorded losses: Food & Beverage saw -4.79 USD billion (-4%), and Consumer experienced -18.02 USD billion (-14.9%).